Study On The Pattern Recognition Methods Of Gas Sensor Array Based On Support Vector Machine

As the advancement of science and technology and the development of industry, the detection of gases requires not only fast, accurate, but also testing on-line. Caused by the cross-sensitivity of the gas sensors, it is impossible for a single gas sensor to identify multiple gases. So, identification of multi-component gas based on gas sensor array and pattern recognition is becoming an important way in dealing with cross-sensitivity in gas analysis. The pattern recognition technology plays the crucial role to the recognition effect.Taking the detection of six kinds of transformer oil dissolved gas (H2, CO, CH4, C2H4, C2H2, C2H6) as an example, this article proposed a pattern recognition method of gas sensor array based on immune weighted support vector machine for regression. The current research situation and commonly used pattern recognition methods are described at the beginning of the thesis. Characteristic experiments of MQ type gas sensors are carried. The author introduced the theory of support vector machine and pattern recognition method of gas sensor array based on SVM is studied step by step. The theory of SVM is used to do quantitative analysis after obtaining the multi-dimensional information of the gas composition and concentration by gas sensor array.Firstly, characteristic experiments of MQ type gas sensors are carried. Research shows that these gas sensors have good performance on sensitivity, repeatability and response characteristic. But cross sensitivity also exists at the same time.Secondly, aiming at the problems such as difficult determination of net structure and local minimization of neural networks, the BP neural networks and the standard SVR are used in the gas sensor array signals pattern recognition. The result shows the values of standard SVR is better than BP neural networks. Immune algorithm is used in the SVM parameters optimization process. Because of immune algorithm can maintain the diversity of the group, the parameters can be adjusted globally and use it in sensor array signals pattern recognition. Results show that, this kind of pattern recognition method overcomes the shortages of the trial-and-error method, and improves recognition accuracy of SVM.Finally, aiming at the problem of no considering the importance of each sample in the standard SVR, each training sample is assigned different approximation error requirement and different penalty. the thesis take linear interpolation method and nonlinear interpolation method to the weighted parameters and use the SVM which combine immune algorithm with parameters weighted in the gas sensor array signals pattern recognition. The results show that, immune weighted support vector machine have better recognition accuracy, performance and application prospect.